{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Deep Learning Book (Simplified)\n",
    "## Part II - Modern Practical Deep Networks\n",
    "*This is a series of blog posts on the [Deep Learning book](http://deeplearningbook.org)\n",
    "where we are attempting to provide a summary of each chapter highlighting the concepts \n",
    "that we found to be most important so that other people can use it as a starting point\n",
    "for reading the chapters, while adding further explanations on few areas that we found difficult to grasp. Please refer [this](http://www.deeplearningbook.org/contents/notation.html) for more clarity on \n",
    "notation.*\n",
    "\n",
    "\n",
    "## Chapter 9: Convolutional Networks\n",
    "\n",
    "**Convolutional networks**, also known as **convolutional neural networks**, or **CNN**s, are a specialized kind of neural network for processing data that has a known grid-like topology. <br>\n",
    "\n",
    "The chapter is organized as follows:\n",
    "\n",
    "**1. The Convolution Operation** <br>\n",
    "**2. Motivation** <br>\n",
    "**3. Pooling** <br>\n",
    "**4. Convolution and Pooling as an Infinitely Strong Prior** <br>\n",
    "**5. Variants of the Basic Convolution Function** <br>\n",
    "**6. Structured Outputs** <br>\n",
    "**7. Data Types** <br>\n",
    "**8. Efficient Convolution Algorithms**<br>\n",
    "**9. Random and Unsupervised Features**<br>\n",
    "**10. The Neuroscientific Basis for Convolutional Networks**<br>\n",
    "**11. Convolutional Networks and the History of Deep Learning**<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. The Convolution Operation\n",
    "\n",
    "- The convolution operates on the **input** with a **kernel** (weights) to produce an **output map** given by:\n",
    "$$ s(t) = \\int x(a)w(t-a)da $$\n",
    "or in discrete space as:\n",
    "$$ s(t) = \\sum_{a=-\\infty}^{\\infty} x(a)w(t-a) $$\n",
    "and in 2D as:\n",
    "$$ s(i,j) = \\sum_m \\sum_n x(m,n)w(i-m,j-n) $$\n",
    "- The flipping of the kernel weights gives the formulation the commutative property, i.e.\n",
    "\n",
    "$$ s(i,j) = \\sum_m \\sum_n x(m,n)w(i-m,j-n) = \\sum_m \\sum_n x(i-m,j-n)w(m,n) $$\n",
    "\n",
    "- When the kernel isn't flipped, it results in the **cross-correlation** (this operation however lacks the commutative property):\n",
    "\n",
    "$$ s(i,j) = \\sum_m \\sum_n x(i+m,j+n)w(m,n) $$\n",
    "\n",
    "- The operation can be broken into matrix multiplications using the **Toeplitz** matrix representation for 1D and **block-circulant** matrix for 2D convolution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Motivation\n",
    "\n",
    "- **Sparse interactions**: Each output unit is connected to (affected by) only a subset of the input units. This significantly reduces the number of parameters and hence the number of computations in the operation. In  *deep CNNs*, the units in the deeper layers interact *indirectly* with large subsets of the input which allows modelling of complex interactions through sparse connections.\n",
    "\n",
    "- **Parameter sharing**: In a CNN each kernel weight is used at every input position (except maybe at boundaries where different padding rules apply). It can be seen easily that if the same linear operation needs to be applied at all positions in an input image, the convoution representations is much more economical as compared to the equivalent fully-connected variant. Less parameters also implies more statistical efficiency."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* m = number of input units\n",
    "* n = number of output units\n",
    "* k = kernel size\n",
    "* l = number of kernels in the set (for tiled convolution)\n",
    "\n",
    "| Type | Computations | Parameters |\n",
    "| --- | --- | --- |\n",
    "| Fully connected | ***O(mn)*** | ***O(mn)*** |\n",
    "| Locally connected | ***O(kn)*** | ***O(kn)*** |\n",
    "| Tiled | ***O(kn)*** | ***O(kl)*** |\n",
    "| Traditional | ***O(kn)*** | ***O(k)*** |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Equivariance**: Parameter sharing also provides **equivariance to translation**\n",
    "    - A function *f* is said to be equivariant to a function *g* if $f(g(x)) = g(f(x))$ i.e. if input changes, the output changes in the same way\n",
    "    - Here we see the translation of the image results in corresponding translation in the output map (except maybe for boundary pixels)\n",
    "    - Note that convolution operation by itself is not equivariant to changes in scale or rotation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:19: DeprecationWarning: `imrotate` is deprecated!\n",
      "`imrotate` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.rotate`` instead.\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:20: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy import signal\n",
    "from scipy import misc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# %matplotlib inline\n",
    "\n",
    "img = misc.ascent()\n",
    "kernel = np.random.randn(5,5)\n",
    "# kernel = np.array([[0,-10,0,10,0],[-10,-30,0,30,10],[0,-10,0,10,0]])\n",
    "\n",
    "img = img.astype(np.float32)/255\n",
    "orig_in = img\n",
    "\n",
    "offsetx = offsety = 20\n",
    "shift_in = np.zeros(orig_in.shape)\n",
    "shift_in[offsetx:,offsety:] = img[:-offsetx,:-offsety]\n",
    "\n",
    "rot_in = misc.imrotate(img, 90)\n",
    "scale_in = misc.imresize(orig_in, 1.5)\n",
    "\n",
    "output1 = signal.convolve2d(orig_in, kernel, mode='same')\n",
    "output2 = signal.convolve2d(shift_in, kernel, mode='same')\n",
    "output3 = signal.convolve2d(rot_in, kernel, mode='same')\n",
    "output4 = signal.convolve2d(scale_in, kernel, mode='same')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:27: DeprecationWarning: `imrotate` is deprecated!\n",
      "`imrotate` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.rotate`` instead.\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:30: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1e5aa2fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 4, figsize=(14, 7))\n",
    "ax_orig = axes[0,0]\n",
    "ax_shift = axes[0,1]\n",
    "ax_rot = axes[0,2]\n",
    "ax_scale = axes[0,3]\n",
    "\n",
    "diff_orig = axes[1,0]\n",
    "diff_shift = axes[1,1]\n",
    "diff_rot = axes[1,2]\n",
    "diff_scale = axes[1,3]\n",
    "\n",
    "ax_orig.imshow(output1, cmap='gray')\n",
    "ax_orig.set_title('Original')\n",
    "ax_shift.imshow(output2, cmap='gray')\n",
    "ax_shift.set_title('Shifted')\n",
    "ax_rot.imshow(output3, cmap='gray')\n",
    "ax_rot.set_title('Rotated')\n",
    "ax_scale.imshow(output4, cmap='gray')\n",
    "ax_scale.set_title('Scaled')\n",
    "\n",
    "def shift(arr, offset):\n",
    "    output = np.zeros(arr.shape)\n",
    "    output[offset:, offset:] = arr[:-offset,:-offset]\n",
    "    return output\n",
    "\n",
    "def rotate(arr, angle):\n",
    "    return misc.imrotate(arr, angle)\n",
    "\n",
    "def resize(arr, scale):\n",
    "    return misc.imresize(arr, scale)\n",
    "\n",
    "diff_orig.hist(np.ravel(output1),bins=100)\n",
    "diff_orig.set_title('Output histogram')\n",
    "diff_shift.hist(np.ravel(np.abs(output2-shift(output1, 20))),bins=100)\n",
    "diff_shift.set_title('Shift histogram difference')\n",
    "diff_rot.hist(np.ravel(np.abs(output3-rotate(output1, 10))),bins=100)\n",
    "diff_rot.set_title('Rotate histogram difference')\n",
    "diff_scale.hist(np.ravel(np.abs(output4-resize(output1, 1.5))),bins=100)\n",
    "diff_scale.set_title('Scale histogram difference')\n",
    "\n",
    "ax_orig.set_xticks([])\n",
    "ax_shift.set_xticks([])\n",
    "ax_rot.set_xticks([])\n",
    "ax_scale.set_xticks([])\n",
    "\n",
    "ax_orig.set_yticks([])\n",
    "ax_shift.set_yticks([])\n",
    "ax_rot.set_yticks([])\n",
    "ax_scale.set_yticks([])\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.show()\n",
    "plt.savefig('images/conv_equivariance.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Pooling\n",
    "\n",
    "A convolutional layer can be broken into the following components:\n",
    "\n",
    "1. Convolution\n",
    "2. Activation (detector stage)\n",
    "3. Pooling\n",
    "\n",
    "\n",
    "- The pooing function calculates a **summary statistic** of the nearby pixels at the point of operation. Several common statistics are max, mean, weighted average and $L^2$ norm of a surrounding rectangular window.\n",
    "- Pooling makes the representation slightly **translation invariant** in that small translations in the input do not cause large changes in the output map. It allows detection of a particular feature if we only care about its existence, not its position in an image. This is a strong requirement on the representation learnt.\n",
    "- Pooling over feature channels can be used to develop invariance to certain transformations of the input. For e.g., units in a layer may be developed to learn rotated features and then pooled over. This property has been used in [maxout networks](http://proceedings.mlr.press/v28/goodfellow13.pdf)\n",
    "- Pooling reduces the input size to the next layer in turn reducing the number of computations required upstream.\n",
    "- Variable sized inputs are an issue when presented to a fully connected layer. To counter this, the pooling operation maybe performed on regions of the input (such as quadrants) thus allowing the model to work on variable sized inputs.\n",
    "\n",
    "\n",
    "[Theoretical guidelines](http://www.di.ens.fr/willow/pdfs/icml2010b.pdf) for which pooling to use have been studied. [Dynamic pooling](http://yann.lecun.com/exdb/publis/pdf/boureau-iccv-11.pdf) has also been studied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1e599ead0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(101)\n",
    "\n",
    "from scipy import signal\n",
    "from scipy import misc\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "img = misc.ascent()\n",
    "img = img.astype(np.float32)/255\n",
    "\n",
    "# The image is more interesting here\n",
    "orig_in = img[-200:,-300:-100]\n",
    "offsetx = offsety = 15\n",
    "shift_in = img[-200-offsetx:-offsetx,-300-offsety:-100-offsety]\n",
    "kernel1 = np.random.randn(5,5)\n",
    "kernel2 = np.random.randn(5,5)\n",
    "kernel3 = np.random.randn(5,5)\n",
    "\n",
    "def sigmoid(arr):\n",
    "    # Lazy implementation of sigmoid activation\n",
    "    return 1./(1 + np.exp(-arr))\n",
    "\n",
    "def maxpool(arr, poolsize, stride):\n",
    "    # Lazy looping implementation of maxpool\n",
    "    output_shape = np.floor((np.array(arr.shape)-poolsize)/stride)+1\n",
    "    output_shape = output_shape.astype(np.int32)\n",
    "    output = np.zeros(output_shape)\n",
    "    for x in range(output_shape[0]):\n",
    "        for y in range(output_shape[1]):\n",
    "            output[x,y] = np.max(arr[x*stride:x*stride+poolsize,y*stride:y*stride+poolsize])\n",
    "    return output\n",
    "\n",
    "output1_1 = signal.convolve2d(orig_in, kernel1, mode='valid')\n",
    "pool1_1 = maxpool(output1_1, 2, 2)\n",
    "actv1_1 = sigmoid(pool1_1)\n",
    "output1_2 = signal.convolve2d(actv1_1, kernel2, mode='valid')\n",
    "pool1_2 = maxpool(output1_2, 2, 2)\n",
    "actv1_2 = sigmoid(pool1_2)\n",
    "output1_3 = signal.convolve2d(actv1_2, kernel3, mode='valid')\n",
    "pool1_3 = maxpool(output1_3, 2, 2)\n",
    "\n",
    "output2_1 = signal.convolve2d(shift_in, kernel1, mode='valid')\n",
    "pool2_1 = maxpool(output2_1, 2, 2)\n",
    "actv2_1 = sigmoid(pool2_1)\n",
    "output2_2 = signal.convolve2d(actv2_1, kernel2, mode='valid')\n",
    "pool2_2 = maxpool(output2_2, 2, 2)\n",
    "actv2_2 = sigmoid(pool2_2)\n",
    "output2_3 = signal.convolve2d(actv2_2, kernel3, mode='valid')\n",
    "pool2_3 = maxpool(output2_3, 2, 2)\n",
    "\n",
    "fig, axes = plt.subplots(4, 3, figsize=(10, 10))\n",
    "\n",
    "k1, k2, k3 = axes[0,:]\n",
    "p1_1, p1_2, p1_3 = axes[1,:]\n",
    "p2_1, p2_2, p2_3 = axes[2,:]\n",
    "h1, h2, h3 = axes[3,:]\n",
    "\n",
    "k1.imshow(kernel1, cmap='gray')\n",
    "k1.set_title('kernel1')\n",
    "k2.imshow(kernel2, cmap='gray')\n",
    "k2.set_title('kernel2')\n",
    "k3.imshow(kernel3, cmap='gray')\n",
    "k3.set_title('kernel3')\n",
    "k1.set_xticks([])\n",
    "k2.set_xticks([])\n",
    "k3.set_xticks([])\n",
    "k1.set_yticks([])\n",
    "k2.set_yticks([])\n",
    "k3.set_yticks([])\n",
    "\n",
    "p1_1.imshow(pool1_1, cmap='gray')\n",
    "p1_1.set_title('pool1_1')\n",
    "p1_2.imshow(pool1_2, cmap='gray')\n",
    "p1_2.set_title('pool1_2')\n",
    "p1_3.imshow(pool1_3, cmap='gray')\n",
    "p1_3.set_title('pool1_3')\n",
    "p1_1.set_xticks([])\n",
    "p1_2.set_xticks([])\n",
    "p1_3.set_xticks([])\n",
    "p1_1.set_yticks([])\n",
    "p1_2.set_yticks([])\n",
    "p1_3.set_yticks([])\n",
    "\n",
    "p2_1.imshow(pool2_1, cmap='gray')\n",
    "p2_1.set_title('pool2_1')\n",
    "p2_2.imshow(pool2_2, cmap='gray')\n",
    "p2_2.set_title('pool2_2')\n",
    "p2_3.imshow(pool2_3, cmap='gray')\n",
    "p2_3.set_title('pool2_3')\n",
    "p2_1.set_xticks([])\n",
    "p2_2.set_xticks([])\n",
    "p2_3.set_xticks([])\n",
    "p2_1.set_yticks([])\n",
    "p2_2.set_yticks([])\n",
    "p2_3.set_yticks([])\n",
    "\n",
    "h1.hist(np.ravel(np.abs(pool1_1-pool2_1)),bins=100)\n",
    "h1.set_title('Pool 1 diff')\n",
    "h2.hist(np.ravel(np.abs(pool1_2-pool2_2)),bins=100)\n",
    "h2.set_title('Pool 2 diff')\n",
    "h3.hist(np.ravel(np.abs(pool1_3-pool2_3)),bins=100)\n",
    "h3.set_title('Pool 3 diff')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.show()\n",
    "plt.savefig('images/pool_invariance.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Convolution and Pooling as an Infinitely Strong Prior\n",
    "\n",
    "**What is a weight prior?** Assumptions about the weights (before learning) in terms of acceptable values and range are encoded into the *prior* distribution of the weights. A *weak prior* is has a high variance and shows that there is low confidence in the initial value of the weight. A *strong prior* is turn shows a narrow range of values about which we are confident before learning begins. An *infinitely strong prior* demarkates certain values as forbidden completely assigning them zero probability.\n",
    "\n",
    "If we view the convolutional layer as a fully connected layer, **convolution imposes an infinitely strong prior** by making the following restrictions on the weights:\n",
    "1. Adjacent units must have the same weight but shifted in space\n",
    "2. Except for a small spatially connected region, all other weights must be zero\n",
    "\n",
    "\n",
    "Likewise the **pooling stage imposes an infinitely strong prior** by requiring features to be translation invariant.\n",
    "\n",
    "Insights:\n",
    "1. Conv and pooling can cause underfitting if the priors imposed are not suitable for the task.\n",
    "2. Convolutional models should only be compared with other convolutional models. This is because other models which are **permutation invariant** can learn even when input features are permuted (thus loosing spatial relationships). Such models need to learn these spatial relationships (which are hard coded in CNNs). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Variants of the Basic Convolution Function\n",
    "\n",
    "In practical implementations of the convolution operation, certain modifications are made which deviate from the discrete convolution formula mentioned above:\n",
    "\n",
    "- In general a convolution layer consists of application of several different kernels to the input. This allows the extraction of several different features at all locations in the input\n",
    "- The input is generally not real-valued but instead vector valued (e.g. RGB values at each pixel or the feature values computed by the previous layer at each pixel position). Multichannel convolutions are commutative only if number of output and input channels is the same.\n",
    "- In order to allow for calculation of features at a *coarser level* strided convolutions can be used. The effect of strided convolution is the same as that of a convolution followed by a downsampling stage. This can be used to reduce the representation size.\n",
    "- Zero padding helps to make output dimensions and kernel size independent. 3 common zero padding strategies are:\n",
    "    1. **valid**: The output is computed only at places where the entire kernel lies inside the input. Essentially, no zero padding is performed. For a kernel of size *k* in any dimension, the input shape of *m* in the direction will become *m-k+1* in the output. This shrinkage restricts architecture depth.\n",
    "    2. **same**: The input is zero padded such that the spatial size of the input and output is same. Essentially, for a dimension where kernle size is *k*, the input is padded by *k-1* zeros in that dimension. Since the number of output units connected to border pixels is less than that for centre pixels, it may under-represent border pixels.\n",
    "    3. **full**: The input is padded by enough zeros such that each input pixel is connected to the same number of output units.\n",
    "    \n",
    "    In  terms of test set accuracy, the optimal padding is somewhere between *same* and *valid*.\n",
    "- **Locally connected layer/unshared convolution**: The connectivity graph of convolution operation and locally connected layer is the same. The only difference is that parameter sharing is not performed, i.e. each output unit performs a linear operation on its neighbourhood but the parameters are not shared across output units. This allows models to capture local connectivity while allowing different features to be computed at different spatial locations. This however requires much more parameters than the convolution operation.\n",
    "- **Tiled convolution** is a sort of middle step between locally connected layer and traditional convolution. It uses a set of kernels that are cycled through. This reduces the number of parameters in the model while allowing for some freedom provided by unshared convolution.\n",
    "- Another extension can be to restrict the kernels to operate on certain input channels. One way to implement this is to connect the first *m* input channels to the first *n* output channels, the next *m* input channels to the next *n* output channels and so on. This method decreases the number of parameters in the model without dereasing the number of output units.\n",
    "- **NOTE**: When max pooling operation is applied to locally connected layer or tiled convolution, the model has the ability to become *transformation invariant* because adjacent filters have the freedom to learn a transformed version of the same feature. This essentially similar to the property leveraged by pooling over channels rather than spatially.\n",
    "- **Bias** terms can be used in different ways in the convolution stage. For locally connected layer and tiled convolution, we can use a bias per output unit and kernel respectively. In case of traditional convolution, a single bias term per output channel is used. If the input size is fixed, a bias per output unit may be used to counter the effect of regional image statistics and smaller activations at the boundary due to zero padding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.x Backpropogation through the convolution layer\n",
    "\n",
    "For a convolutional kernel with kernel stack $K$, multichannel input $V$ and stride $s$, the output $Z$ is given by\n",
    "\n",
    "\\begin{equation} Z_{i,j,k} = c(K,V,s)_{i,j,k} = \\sum_{l,m,n} \\big[ V_{l,(j-1)\\times s+m,(k-1)\\times s+n}K_{i,l,m,n} \\big] \\end{equation}\n",
    "\n",
    "Suppose we want to minimize loss $J(V,K)$. During back propogation, the layer receives the tensor\n",
    "\n",
    "\\begin{equation} G_{i,j,k} = \\frac{\\partial}{\\partial Z_{i,j,k}}J(V,K) \\end{equation}\n",
    "\n",
    "To compute gradient with respect to kernel parameters, we compute:\n",
    "\n",
    "\\begin{equation} g(G,V,s)_{i,j,k,l} = \\frac{\\partial}{\\partial K_{i,j,k,l}}J(V,K) = \\sum_{m,n} G_{i,m,n}V_{j,(m-1)\\times s+k, (n-1)\\times s+l} \\end{equation}\n",
    "\n",
    "To backpropogate the error to the lower layer, we compute:\n",
    "\n",
    "\\begin{equation} h(K,G,s)_{i,j,k} = \\frac{\\partial}{\\partial V_{i,j,k}}J(V,K) \\\\\n",
    "= \\sum_{\\substack{l,m \\\\ s.t. \\\\ (l-1)\\times s+m=j}} \\sum_{\\substack{n,p \\\\ s.t. \\\\ (n-1)\\times s+p=k}} \\sum_{q} K_{q,i,m,p}G_{q,l,n} \\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Structured Outputs\n",
    "\n",
    "Convulutional networks can be trained to output high-dimensional structured output rather than just a classification score. A good example is the task of image segmentation where each pixel needs to be associated with an object class. Here the output is the same size (spatially) as the input. The model outputs a tensor $S$ where $S_{i,j,k}$ is the probability that pixel *(j,k)* belongs to class *i*.\n",
    "\n",
    "- To produce an output map as the same size as the input map, only **same-padded** convolutions can be stacked.\n",
    "- Alternatively, a coarser segmentation map can be obtained by allowing the output map to shrink spatially\n",
    "\n",
    "The output of the first labelling stage can be refined successively by another convolutional model. If the models use tied parameters, this gives rise to a type of **recursive model** as formulated below. The subscript $W$ shows that the weights are shared across the iterations. The $O$ input in the first step is a dummy variable since $Y(0)$ is unavailable.\n",
    "\n",
    "$$ Y(1) = H_{W}(X, O) $$\n",
    "$$ Y(2) = H_{W}(X, Y(1)) $$\n",
    "$$ Y(3) = H_{W}(X, Y(2)) $$\n",
    "$$ ... $$\n",
    "\n",
    "The output can be further processed under the assumption that contiguous regions of pixels will tend to belong to the same label. Graphical models can describe this relationship. Alternately, [CNNs can learn to optimize the graphical models training objective](https://www.robots.ox.ac.uk/~vgg/rg/papers/tompson2014.pdf)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Data Types\n",
    "\n",
    "> The data used with a convolutional network usually consist of several channels, each channel being the observation of a different quantity at some point in space or time\n",
    "\n",
    "> One advantage to convolutional networks is that they can also process inputs with varying spatial extents.\n",
    "\n",
    "When the output is accordingly variable sized, no extra design change needs to be made. If however the output is fixed sized, as in the classification task, a pooling stage with kernel size proportional to the input size needs to be used.\n",
    "\n",
    "Different data types based on number of spatial dimensions and channels are listed:\n",
    "\n",
    "| Dimensions | Single channel | Multichannel |\n",
    "| --- | --- | --- |\n",
    "| 1D | **Raw audio** (single amplitude value per time point) | **Skeleton animatin data** (orientation of each joint) |\n",
    "| 2D | **Audio spectrogram** (one FFT coefficient per time point per frequency) | **Color image** (RGB triplet per (x,y) tuple) |\n",
    "| 3D | **CT scan** (one value per (x,y,z) tuple) | **Color video** (one RGB triplet per (x,y) tuple per time instant) |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Efficient Convolution Algorithms\n",
    "\n",
    "In some problem settings, performing convolution as pointwise multiplication in the frequency domain can provide a speed up as compared to direct computation.\n",
    "\n",
    "When a *d*-dimensional kernel can be broken into the outer product of *d* vectors, the kernel is said to be separable. The corresponding convolution operations are more efficient when implemented as *d* 1-dimensional convolutions rather than a direct *d*-dimensional convolution.\n",
    "\n",
    "> Devising faster ways of performing convolution or approximate convolution without harming the accuracy of the model is an active area of research"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Random and Unsupervised Features\n",
    "\n",
    "To reduce the computational cost of training the CNN, we can use features not learned by supervised training.\n",
    "\n",
    "1. **Random initialization** has been shown to create filters that are [frequency selective and translation invariant](http://www.robotics.stanford.edu/~ang/papers/nipsdlufl10-RandomWeights.pdf). This can be used to inexpesively select the model architecture. Randomly initialize several CNN architectures and just train the last classification layer. Once a winner is determined, that model can be fully trained in a supervised manner.\n",
    "2. **Hand designed kernels** may be used; e.g. to detect edges at different orientations and intensities\n",
    "3. **Unsupervised training** of kernels may be performed; e.g. [applying k-means clustering to image patches](https://www-cs.stanford.edu/people/ang/papers/nipsdlufl10-AnalysisSingleLayerUnsupervisedFeatureLearning.pdf) and using the centroids as convolutional kernels. Unsupervised pretraining may offer regularization effect (not well established). It may also allow for training of larger CNNs because of reduced computation cost.\n",
    "\n",
    "Another approach for CNN training is **greedy layer-wise pretraining** most notably used in [convolutional deep belief network](https://ai.stanford.edu/~ang/papers/icml09-ConvolutionalDeepBeliefNetworks.pdf). As in the case of multi-layer perceptrons, starting with the first, each layer is trained in isolation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. The Neuroscientific Basis for Convolutional Networks\n",
    "\n",
    "Hubel and Wiesel studied the activity of neurons in a cat's brain in response to visual stimuli. Their work characterized many aspects of brain function.\n",
    "\n",
    "In a simplified view, we have:\n",
    "1. The light entering the eye stimulates the **retina**. The image then passes through the the optic nerve and a region of the brain called the **LGN (lateral geniculate nucleus)**\n",
    "2. **V1 (primary visual cortex)**: The image produced on the retina is transported to the V1 with minimal processing. The properties of V1 that have been replicated in CNNs are\n",
    "    - The V1 response is localized spatially, i.e. the upper image stimulates the cells in the upper region of V1 **[kernel]**\n",
    "    - V1 has simple cells whose activity is a linear function of the input in a small neighbourhood **[convolution]**\n",
    "    - V1 has complex cells whose activity is invariant to shifts in the position of the feature **[pooling]** as well as some changes in lighting which cannot be captured by spatial pooling **[cross-channel pooling]**\n",
    "3. There are several stages of V1 like operations.\n",
    "4. In the *medial temporal lobe*, we find **grandmother cells**. These cells respond to specific concepts and are invariant to several transforms of the input. In the *medial temporal lobe*, researchers also found neurons spiking on a particular concept, e.g. the *Halle Berry neuron* fires when looking at a photo/drawing of *Halle Berry* or even reading the text *Halle Berry*.\n",
    "\n",
    "The medial temporal neurons are more generic than CNN in that they respond even to specific ideas. A closer match to the function of the last layers of a CNN is the **IT (inferotemporal cortex)**. When viewing an object, information flows from the retina, through LGN, V1, V2, V4 and reaches IT. This happens within 100ms. When a person continues to look at an object, the brain sends top-down feedback signals to affect lower level activation.\n",
    "\n",
    "Some of the major differences between the human visual system (HVS) and the CNN model are:\n",
    "- The human eye is low resolution except in a region called **fovea**. Essentially, the eye does not receive the whole image at high resolution but stiches several patches through eye movements called **saccades**. This attention based gazing of the input image is an active research problem. Note: attention mechanisms have been shown to work on natural language tasks.\n",
    "- Integration of several senses in the HVS while CNNs are only visual\n",
    "- The HVS processes rich 3D information, and can also determine relations between objects. CNNs for such tasks are in their early stages.\n",
    "- The feedback from higher levels to V1 has not been incorporated into CNNs with substantial improvement.\n",
    "- While the  CNN can capture firing rates in the IT, the similarity between intermediate computations is not established. The brain probably uses different activation and pooling functions. Even the linearity of filter response is doubtful as recent models for V1 involve quadratic filters.\n",
    "\n",
    "Neuroscience tells us very little about the training procedure. Backpropogation which is a standard training mechanism today is not inspired by neuroscience and sometimes considered biologically implausible.\n",
    "\n",
    "In order to determine the filter parameters used by neurons, a process called **reverse correlation** is used. The neuron activations are measured by an electrode when viewing several white noise images and a linear model is used to approximate this behaviour. It has been shown experimentally that the weights of the fitted model of V1 neurons are described by **Gabor functions**. If we go by the simplified version of the HVS, if the simple cells detect Gabor-like features, then complex cells learn a function of simple cell outputs which is invariant to certain translations and magnitude changes.\n",
    "\n",
    "A wide variety of statistical learning algorithms (from unsupervised (sparse code) to deep learning (first layer features)) learn features with Gabor-like functions when applied to natural images. This goes to show that while no algorithm can be touted as the *right method* based on Gabor-like feature detectors, a lack of such features may be taken as a *bad sign*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Convolutional Networks and the History of Deep Learning\n",
    "\n",
    "Summarizing a [summary](http://www.deeplearningbook.org/contents/convnets.html#pf28) would be a redundant task."
   ]
  }
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